评估人工智能生成的临床笔记的质量:对大型语言模型的验证评估
Erin Palm1,2,3, Astrit Manikantan1, Herprit Mahal1,4,5
1Suki AI, Redwood City, CA, United States.
Frontiers in artificial intelligence
|November 7, 2025
概括
生成型人工智能 (AI) 的临床笔记显示质量与医生撰写的笔记相当,在彻底性和组织方面表现出色,但幻觉更多. 医生文档质量仪器 (PDQI-9) 为评估AI文档质量提供了一个框架.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 生成型人工智能 (AI) 越来越多地被用来作为人工智能.
- 环境的书记是环境的书记.
- 对于临床笔记生成.
- 与医生标准相比,对人工智能生成的文档质量的系统评估是有限的.
研究的目的:
- 将大型语言模型 (LLM) 生成的临床笔记 (环境) 与医生撰写的笔记 (黄金) 的质量进行比较.
- 使用医生文档质量仪器 (PDQI-9) 来评估五个临床专业的笔记质量.
主要方法:
- 汇集了97个未经识别的门诊临床接触的音频录音.
- 生成的两个LLM优化的优化.
- 环境环境环境环境环境
- 和医生起草的法律.
- 黄金 黄金 黄金 黄金
- 笔记. 笔记. 这些笔记.
- 两名盲人审稿人使用修改后的PDQI-9评估了笔记,并检测到了幻觉.
- 评估评价者之间的可靠性并进行对对比.
主要成果:
- 观察到较高的虫间一致性 (RWG > 0.7).
- 黄金纸币在整体质量,准确性,简洁性和内部一致性方面得分更高.
- 环境笔记在彻底性和组织性方面得分更高.
- 在31%的环境纸币中检测到幻觉,而在黄金纸币中则只有20%.
- 评论员总体上更喜欢环境笔记 (47%对39%).
结论:
- 通过LLM生成的笔记显示出与医生撰写的笔记相当的质量.
- 环境笔记更彻底,更有组织,但不那么简洁,容易产生幻觉.
- PDQI-9 是一个验证的框架,用于评估人工智能生成的临床文档质量.
- 这种方法可以为环境AI书记的优化和标准化提供信息.
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